Getting Started¶

To install SpatialTis, please see the Installation section. It’s recommended to start a new virtual environment before installing the SpatialTis.

Prerequisites¶

Must learn:

anndata: Tutorial written by the author of anndata and scanpy

Optional:

numpy: For array operation
pandas: For table operation
scanpy: Used for cell type calling and scRNA-seq related analysis

Basic Usage¶

A recommended way to import SpatialTis

>>> import spatialtis as st
>>> import spatialtis.plotting as sp

Let’s load some data for analysis, SpatialTis takes AnnData as input,

>>> from anndata import read_h5ad
>>> data = read_h5ad('seqFISH.h5ad')
>>> data
AnnData object with n_obs × n_vars = 913 × 9566
    obs: 'Field of View', 'centroid', 'Region', 'cell_type'
    var: 'markers'

We could start to construct the neighbors network and profile cell-cell interactions.

>>> st.find_neighbors(data,
...                   r=180,
...                   exp_obs=['Region', 'Field of View'],
...                   centroid_key='centroid')
⏳ Find neighbors
🛠 Method: kdtree
📦 Added to AnnData, obs: 'cell_neighbors'
📦 Added to AnnData, obs: 'cell_neighbors_count'
⏱ 10ms

>>> cci = st.cell_interactions(data,
...                            exp_obs = ['Region', 'Field of View'],
...                            centroid_key='centroid',
...                            cell_type_key='cell_type')
⏳ Cell interaction
📦 Added to AnnData, uns: 'cell_interaction'
⏱ 164ms

Setting Configurations¶

Notice that we repeatedly enter exp_obs and centroid_key twice. Obviously, we don’t want to write these for every analysis. SpatialTis allows you to set it via Global config.

>>> from spatialtis import Config
>>> Config.exp_obs = ['Region', 'Field of View']
>>> Config.roi_key = 'Field of View'
>>> Config.cell_type_key = 'cell_type'
>>> Config.centroid_key = 'centroid'
>>> Config.marker_key = 'markers'

Now we could run the analysis in a much cleaner way.

>>> _ = st.find_neighbors(data)
>>> cci = st.cell_interaction(data)

Save and get results¶

To access the result of cell-cell interactions analysis, you could use result attribute that’s available for every analysis object.

>>> result = cci.result

Or you could call the get_result to get it from AnnData.uns

>>> result = st.get_result(data, 'cell_interaction')

To visualize the analysis.

>>> sp.cell_interaction(data)

Input data¶

SpatialTis required cell coordination stored in wkt (well-known text) format, which could be easily serialized and deserialized.

If you have two columns X and Y in AnnData.obs that store x, y coordination, you could transform them into wkt format:

>>> st.transform_points(data, ('X', 'Y'), export_key='wkt_centroid')

Or if you save your coordination in single column centroid, transform it like:

>>> st.transform_points(data, 'centroid', export_key='wkt_centroid')

If you have shape information, which should be stored as multipolygons. You should have one columns ‘shape’ that store a series of points in a array container like [(1, 2), (3, 4), ..., (100, 100)], transform it like:

>>> st.transform_shapes(data, 'shape', export_key='wkt_shape')

SpatialTis 0.4.0 documentation